Human Computing: Modelling with Meaning

In bringing the humanities and computing together, the question of how computer science relates to the humanities has to be addressed. Most striking is the starkly different treatment of meaning in the humanities and in computer science. To ignore this issue is to risk investing our limited notion of computer science with unwarranted authority. The commonplace view of computer science suggests a monolithic image of computing, in which all activity reduces to the execution of formal algorithms. Computing in the wild, in contrast, is both incorrigibly plural, and rich in possibilities for marrying a science of computing with a computing of the humanities. This session is designed to explore one such possibility.

The standard way of construing computer science focuses on combinatorics, syntax and algorithms. Its guiding question is "what can be automated?" (Denning). The benefits of asking this question are undeniable — more efficient pattern-matching, more advanced data mining, better data representation and the like. But these benefits, and the question that elicits them, do not address the humanities intellectually. They pertain to a relationship analogous to that between an accountant and his or her calculator — hardly a promising one for computing practitioners and humanities scholars alike. If we wish to have a computing of the humanities, we need to be asking a rather different question: "how can we best integrate automated processing with human thinking and acting?"

Empirical Modelling(EM-website), the approach around which this session has been organized, reflects a radical shift from the logical and linguistic philosophical stance of theoretical computer science to one based on the pragmatic empiricism of William James. It has been developed by two of the authors, Beynon and Russ, at Warwick University. The third author, McCarty, has independently developed a convergent idea of modelling based on the tradition of experimental science, recent historical and philosophical analyses of experiment and the phenomenology of Martin Heidegger, Michael Polanyi and others. The convergence indicates, all will argue, a highly promising basis for interchange between computing science and humanities computing. This basis takes us considerably further than previous attempts. (See e.g. Gardin; Koch; Computing the Future 1992; Computing and the Humanities: Summary of a Roundtable Meeting1998; Orlandi; Beyond Productivity 2003. See also Transforming Disciplines: Computer Science and the Humanities, .)

The first paper discusses the prospects for partnership between the humanities and computing from the alternative perspective afforded by Empirical Modelling. It identifies perceived dualities that separate the two cultures of science and art as the primary impediment to this partnership, and outlines how these can be dissolved in a vision for human computing.

The second paper illustrates the key characteristics and potential for EM for the humanities with reference to a projected modelling exercise addressing the Erlkoenig theme (as represented in the work of Goethe, Schubert and Liszt). It also highlights how each of six varieties of modelling identified by McCarty in (2004) can be represented within an EM model.

The final paper discusses the implications of EM with reference to McCarty's account of the key role for modelling in the humanities (2005), and considers these in relation to James's "philosophic attitude of radical empiricism" and ideas from phenomenological sources.